Approximate computing in the nanoscale era

The reduced benefits offered by technology scaling in the nanoscale era call for innovative design approaches, to process bigger and bigger amount of data with always higher performance and lower power consumption. In this respect, Approximate Computing constitutes one of the most promising trend, where efficiency is increased by breaking the dogma of error-free computations, enlarging the design space with the addition of application-specific quality metrics. Approximate Computing paradigm can be applied at different layers, spanning from software to systems and circuits. In this paper we focus on approximate arithmetic circuits for computer vision and machine learning. These kinds of applications have an excellent resiliency to computation errors. Moreover, their arithmetic-intensive processing makes the increase of efficiency of arithmetic circuits a keypoint. In this kind of context Approximate Computing can make the difference, improving, at the same time, performance and power consumption with tolerable quality degradation.